High Systemic Type I Interferon Activity Is Associated With Active Class III/IV Lupus Nephritis
Bibliographic record
Abstract
Objective Previous studies suggest a link between high serum type I interferon (IFN) and lupus nephritis (LN). We determined whether serum IFN activity is associated with subtypes of LN and studied renal tissues and cells to understand the effect of IFN in LN. Methods Two hundred and twenty-one patients with systemic lupus erythematosus were studied. Serum IFN activity was measured by WISH bioassay. mRNA in situ hybridization was used in renal tissue to measure expression of the representative IFN-induced gene, IFN-induced protein with tetratricopeptide repeats-1 (IFIT1), and the plasmacytoid dendritic cell (pDC) marker gene C-type lectin domain family-4 member C (CLEC4C). Podocyte cell line gene expression was measured by real-time PCR. Results Class III/IV LN prevalence was significantly increased in patients with high serum IFN compared with those with low IFN (odds ratio 5.40, P = 0.009). In multivariate regression models, type I IFN was a stronger predictor of class III/IV LN than complement C3 or anti-dsDNA antibody, and could account for the association of these variables with LN. IFIT1 expression was increased in all classes of LN, but most in the glomerular areas of active class III/IV LN kidneys. IFIT1 expression was not closely colocalized with pDCs. IFN directly activated podocyte cell lines to induce chemokines and proapoptotic molecules. Conclusion Systemic high IFN is involved in the pathogenesis of severe LN. We did not find colocalization of pDCs with IFN signature in renal tissue, and instead observed the greatest intensity of the IFN signature in glomerular areas, which could suggest a blood source of IFN.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".